We estimate a New Keynesian model with matching frictions and nominal wage rigidities on UK data. We are able to identify important structural parameters, recover the unobservable shocks that have affected the UK economy since 1971 and study the transmission mechanism. With matching frictions,...

This paper reviews recent approaches to modelling the labour market, and assesses their implications for inflation dynamics through both their effect on marginal cost and on price-setting behaviour. In a search and matching environment, we consider the following modelling set-ups:...

We assess the importance of downward nominal wage rigidity (DNWR) in Canada using both firm- and worker-level microdata. In particular, we analyze employer-level administrative data from the Major Wage Settlements (MWS) and household-based survey data from the Survey of Labour Income Dynamics...

The number of workers who hold more than one job (a.k.a. multiple jobholders) has increased recently in Canada. While this seems to echo the view that non-standard work arrangements are becoming pervasive, the increase has in fact been trivial compared with the long-run rise of multiple...

This paper examines novel household-level data from the Canadian Survey of Consumer Expectations (CSCE) from 2014Q4 to 2022Q1 to understand households' expectations about price and wage inflation, their respective links to views about labour market conditions and their subsequent impact on...

This paper examines the macroeconomic impact of the first round of quantitative easing (QE) by the Bank of England which started in March 2009. Although Bank Rate, the UK policy rate, was reduced to ½%, effectively its lower bound, the Bank’s Monetary Policy Committee felt that additional...

This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data. The model is then used to generate nowcasts, predictions of the recent past and...

Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as...